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In this paper, we consider the problem of sequential nonlinear regression and introduce an efficient learning algorithm using context trees. Specifically, the regressor space is partitioned and the resulting regions are represented by a context tree. In each region, we assign an independent regression algorithm and the outputs of the all possible nonlinear models defined on the context tree are adaptively...
In this paper, we consider the linear estimation problem under structured data uncertainties. A robust algorithm is presented under bounded uncertainties under the mean square error (MSE) criterion. The performance of the linear estimator is defined relative to the performance of the linear minimum MSE (MMSE) estimator tuned to the underlying unknown data uncertainties, i.e., the introduced algorithm...
In this paper, we present a complete performance analysis of the scalar diffusion strategies over distributed networks. Scalar diffusion strategies are based on the diffusion implementation and adaptive extraction of the information from the diffusion data which is compressed into a scalar. This strategy require significantly less communication load while achieving similar performance with the full...
In this paper, we propose robust set-membership filtering algorithms against impulsive noise. Firstly, we introduce set-membership normalized least absolute difference algorithm (SM-NLAD). This algorithm provides robustness against impulsive noise through pricing the absolute error instead of the square. Then, in order to achieve comparable convergence performance in the impulse-free noise environments,...
We study the steady-state performances of four convexly constrained mixture algorithms that adaptively combine outputs of two adaptive filters running in parallel to model an unknown system. We demonstrate that these algorithms are universal such that they achieve the performance of the best constituent filter in the steady-state if certain algorithmic parameters are chosen properly. We also demonstrate...
This paper proposes a novel estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint...
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